SiGN: Large-Scale Gene Network Estimation Software

Nakazawa, M.A., Tamada, Y., Tanaka, Y., Ikeguchi, M., Higashihara, K., Okuno, Y., (2021). Novel cancer subtyping method based on patient-specific gene regulatory network, Scientific Reports, 11, 23653. (Full text)

Tanaka, Y., Higashihara, K., Nakazawa, M.A., Yamashita, F., Tamada, Y., Okuno, Y., (2021). Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection, Scientific Reports, 11, 11241. (Full text)

Tanaka, Y., Tamada, Y., Ikeguchi, M., Yamashita, F., Okuno, Y. (2020). System-based differential gene network analysis for characterizing a sample-specific subnetwork, Biomolecules, 10 (2), 306. (Full text)

Kasajima, R., Yamaguchi, R., Shimizu, E., Tamada, Y., Niida, A., Tremmel, G., Kishida, T., Aoki, I., Imoto, S., Miyano, S., Uemura, H., Miyagi, Y. (2020). Variant analysis of prostate cancer in Japanese patients and a new attempt to predict related biological pathways, Oncology Reports, 43 (3), 943-952.

Tamada, Y. (2018). Memory Efficient Parallel Algorithm for Optimal DAG Structure Search using Direct Communication, Journal of Parallel and Distributed Computing, 119, 27-35. (Publisher site)

Honda, H.*, Tamada, Y.*, and Suda, R. (2016). Efficient Parallel Algorithm for Optimal DAG Structure Search on Parallel Computer with Torus Network, In Proceedings of the 16th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2016), Lecture Notes in Computer Science, 10048, 483-502. (* These authors contributed equally to this work.) (Publisher site, Conference web site)

Arima, C.^*, Kajino, T.^*, Tamada, Y.^*, Imoto, S., Shimada, Y., Nakatochi, M., Suzuki, M., Isomura, H., Yatabe, Y., Yamaguchi, T., Yanagisawa, K., Miyano, S., and Takahashi, T. (2014). Lung adenocarcinoma subtypes definable by lung development-related miRNA expression profiles in association with clinicopathologic features, Carcinogenesis, 35 (10), 2224-2231. (* These authors contributed equally to this work.) (Publisher site, Pubmed)

Affara, M., Sanders, D., Araki, A., Tamada, Y., Dunmore, B.J., Humphreys, S., Imoto, S., Savoie, C., Miyano, S., Kuhara, S., Jeffries, D., Print, C., and Charnock-Jones, D.S. (2013). Vasohibin-1 is identified as a master-regulator of endothelial cell apoptosis using gene network analysis, BMC Genomics 14 23. (Publisher site, Pubmed)

Ogami, K., Yamaguchi, R., Imoto, S., Tamada, Y., Araki, H., Print C., and Miyano, S. (2012). Computational gene network analysis reveals TNF-induced angiogenesis, BMC Systems Biology 6(Suppl 2), S12. (Publisher site, Pubmed)

Yamauchi, M., Yamaguchi, R., Nakata, A., Kohno, T., Nagasaki, M., Shimamura, T., Imoto, S., Saito, A., Ueno, K., Hatanaka, Y., Yoshida, R., Higuchi, T., Nomura, M., Beer, D.G., Yokota, J., Miyano, S., and Gotoh, N. (2012). Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma. PLoS ONE 7(9), e43923. (Publisher site, Pubmed)

Wang, L., Hurley, D., Watkins, W., Araki, H., Tamada, Y., Muthukaruppan, A., Ranjard, L., Derkac, E., Imoto, S., Miyano, S., Crampin, E., and Print, C.G. (2012). Cell cycle gene networks are associated with melanoma prognosis, PLoS ONE 7(4), e34247. (Publisher site, Pubmed)

Hurley, D., Araki, H., Tamada, Y., Dunmore, B., Sanders, D., Humphreys, S., Affara, M., Imoto, S., Yasuda, K., Tomiyasu, Y., Tashiro, K., Savoie, C., Cho, V., Smith, S., Kuhara, S., Miyano, S., Charnock-Jones, D.S., J. Crampin, E.J., and Print, C.G. (2012). Gene network inference and visualization tools for biologists: application to new human transcriptome datasets, Nucleic Acids Research 40 (6), 2377-2398. (Publisher site, Pubmed)

Tamada, Y., Shimamura, T., Yamaguchi, R., Imoto, S., Nagasaki, M., and Miyano, S. (2011). SiGN: Large-scale gene network estimation environment for high performance computing, Genome Informatics 25 (1), 40-52. (Publisher site, Pubmed)

Tamada, Y., Imoto, S., and Miyano, S. (2011). Parallel algorithm for learning optimal Bayesian network structure, Journal of Machine Learning Research 12, 2437-2459. (Publisher site)

Tamada, Y., Imoto, S., Araki, H., Nagasaki, M., Print, C., Charnock-Jones, D.S., and Miyano, S. (2011). Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers, IEEE/ACM Transactions on Computational Biology and Bioinformatics 8 (3), 683-697. (Publisher site, Pubmed)

Tamada, Y., Yamaguchi, R., Imoto, S., Hirose, O., Yoshida, R., Nagasaki, M., and Miyano, S. (2011). SiGN-SSM: open source parallel software for estimating gene networks with state space models. Bioinformatics 27 (8), 1172-1173. (Publisher site, Pubmed

Shimamura, T., Imoto, S., Shimada, Y., Hosono, Y., Niida, A., Nagasaki, M., Yamaguchi, R., Takahashi, T., Miyano, S. (2011). A novel network profiling analysis reveals system changes in epithelial-mesenchymal transition, PLoS ONE 6(6), e20804. (Publisher site, Pubmed)

Yamaguchi, R., Imoto, S., and Miyano, S. (2010). Network-based predictions and simulations by biological state space models: search for drug mode of action. Journal of Computer Science and Technology 25(1), 131-153. (Publisher site)

Shimamura, T., Imoto, S., Yamaguchi, R., Fujita, A., Nagasaki, M., and Miyano, S. (2009). Recursive regularization for inferring gene networks from time-course gene expression profiles. BMC Systems Biology 3, 41. (Publisher site, Pubmed)

Miyano, S., Yamaguchi, R., Tamada, Y., Nagasaki, M., and Imoto S. (2009). Gene networks viewed through two models. Proceedings of the 1st International Conference on Bioinformatics and Computational Biology (BICoB 2009), Lecture Note in Bioinformatics 4652, 54-66. (Publisher site)

Araki, H., Tamada, Y., Imoto, S., Dunmore, B., Sanders, D., Humphreys, S., Nagasaki, M., Doi, A., Nakanishi, Y., Yasuda, K., Tomiyasu, Y., Tashiro, K., Print, C., Charnock-Jones, D.S., Kuhara, S., and Miyano, S. (2009). Analysis of PPAR alpha-dependent and PPAR alpha-independent transcript regulation following fenofibrate treatment of human endothelial cells. Angiogenesis 12(3), 221-229. (Publisher site, Pubmed)

Tamada, Y., Araki, H., Imoto, S., Nagasaki, M., Doi, A., Nakinishi, Y., Tomiyasu, Y., Yasuda, K., Dunmore, B., Sanders, D., Humphreys, S., Print, C., Charnock-Jones, D.S., Tashiro, K., Kuhara, S., and Miyano, S. (2009). Unraveling dynamic activities of autoacine pathways that control drug-response transcriptome networks. Pacific Symposium on Biocomputing 14 (PSB 2009), 251-263. (Full Text, Pubmed)

Perrier, E., Imoto, S., and Miyano, S. (2008). Finding optimal Bayesian network given a super-structure. Journal of Machine Learning Research 9, 2251-2286. (Publisher site)

Yamaguchi, R., Imoto, S., Yamauchi, M., Nagasaki, M., Yoshida, R., Shimamura, T., Hatanaka, Y., Ueno, K., Higuchi T., Gotoh, N., and Miyano, S. (2008). Predicting differences in gene regulatory systems by state space models. Genome Informatics 21, 101-113. (Publisher site, Pubmed)

Kojima, K., Fujita, A., Shimamura, T., Imoto, S., and Miyano, S. (2008). Estimation of nonlinear gene regulatory networks via L1 regularized NVAR from time series gene expression data. Genome Informatics 20, 37-51. (Publisher site, Pubmed)

Hirose, O., Yoshida, R., Yamaguchi, R., Imoto, S., Higuchi, T., and Miyano, S. (2008). Analyzing time course gene expression data with biological and technical replicates to estimate gene networks by state space models. Proc. 2nd Asia International Conference on Modelling & Simulation (AMS 2008), 940-946. (Publisher site)

Hirose, O., Yoshida, R., Imoto, S., Yamaguchi, R., Higuchi, T., Charnock-Jones, D.S., Print, C., and Miyano, S. (2008). Statistical inference of transcriptional module-based gene enetworks from time course gene expression profiles by using state space models. Bioinformatics 24 (7), 932-942. (Publisher site, Pubmed)

Shimamura, T., Yamaguchi, R., Imoto, S., and Miyano, S. (2007). Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data. Genome Informatics 19 (GIW 2007), 142-153. (Publisher site, Pubmed)

Imoto, S. (2007). Knowledge discovery of causal relations among genes from microarray gene expression data (in Japanese with English abstract), Journal of Japan Statistical Society 37(1), 55-70.

Hirose, O., Yoshida, R., Yamaguchi, R., Imoto, S., Higuchi, T., and Miyano, S. (2007). Clustering with time course gene expression profiles and the mixture of state space models. Genome Informatics 18 (IBSB 2007), 258-266.

Yamaguchi, R., Yoshida, R., Imoto, S., Higuchi, T., and Miyano, S. (2007). Finding module-based gene networks with state-space models - Mining high-dimensional and short time-course gene expression data. IEEE Signal Processing Magazine 24(1), 37-46.

Affara, M., Dunmore, B., Savoie, C.J., Imoto, S., Tamada, Y., Araki, H., Charnock-Jones, D.S., Miyano, S., and Print, C. (2007). Understanding endothelial cell apoptosis: What can the transcriptome glycome and proteome reveal? Philosophical Transactions of Royal Society 62(1484), 1469-1487.

Tamada, Y., Imoto, S., and Miyano, S. (2006). Estimating gene networks from gene expression data utilizing biological information (in Japanese with English abstract). Proc. Inst. Statist. Math. 54(2), 333-356.

Imoto, S., Tamada, Y., Araki, H., Yasuda, K., Print, C.G., Charnock-Jones, S.D., Sanders, D., Savoie, C.J., Tashiro, K., Kuhara, S., and Miyano, S. (2006). Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles. Pacific Symposium on Biocomputing 11 (PSB 2006), 559-571.

Nariai, N., Tamada, Y., Imoto, S., and Miyano, S. (2005). Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. Bioinformatics 21 Suppl.2 (ECCB 2005), ii206-ii212.

Tamada, Y., Imoto, S., Tashiro, K., Kuhara, S., and Miyano, S. (2005). Identifying drug active pathways from gene networks estimated by gene expression data. Genome Informatics 16(1) (IBSB 2005), 182-191.

Tamada, Y., Bannai, H., Imoto, S., Katayama, T., Kanehisa, M., and Miyano, S. (2005). Utilizing evolutionary information and gene expression data for estimating gene regulations with Bayesian network models. Journal of Bioinformatics and Computational Biology 3(6), 1295-1313.

Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., and Miyano, S. (2004). Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. Journal of Bioinformatics and Computational Biology 2(1), 77-98. (extended version of CSB 2003)

Kim, S., Imoto, S., and Miyano, S. (2004). Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems 75(1-3), 57-65. (extension version of CMSB 2003 paper)

Ott, S., Imoto, S., and Miyano, S. (2004). Finding optimal models for small gene networks. Pacific Symposium on Biocomputing 9 (PSB 2004), 557-567. (Full Text, Pubmed)

Nariai, N., Kim, S., Imoto, S., and Miyano, S. (2004). Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks. Pacific Symposium on Biocomputing 9 (PSB 2004), 336-347.

Kim, S., Imoto, S., and Miyano, S. (2003). Inferring gene networks from time series microarray data using dynamic Bayesian networks. Briefings in Bioinformatics 4(3), 228-235.

Imoto, S., Savoie, C.J., Aburatani, S., Kim, S., Tashiro, K., Kuhara, S., and Miyano, S. (2003). Use of gene networks for identifying and validating drug targets. Journal of Bioinformatics and Computational Biology 1(3), 459-474.

Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., and Miyano, S. (2003). Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. Proc. 2nd Computational Systems Bioinformatics (CSB 2003), 104-113.

Tamada, Y., Kim, S., Bannai, H., Imoto, S., Tashiro, K., Kuhara, S., and Miyano, S. (2003). Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics 19 Suppl.2 (ECCB 2003), ii227-ii236. (Publisher Site, Pubmed)

Savoie, C.J., Aburatani, S., Watanabe, S., Eguchi, Y., Muta, S., Imoto, S., Miyano, S., Kuhara, S., and Tashiro, K. (2003). Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades. DNA Research 10, 19-25.

Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S., and Miyano, S. (2003). Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. Journal of Bioinformatics and Computational Biology 1(2), 231-252. (extend version of CSB 2002 paper)

Kim, S., Imoto, S., and Miyano, S. (2003). Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Proc. 1st Computational Methods in Systems Biology (CMSB 2003), Lecture Note in Computer Science 2602, 104-113, Springer-Verlag.

Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S., and Miyano, S. (2002). Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. Proc. 1st IEEE Computer Society Bioinformatics Conference (CSB 2002), 219-227.

Imoto, S., Goto, T., and Miyano, S. (2002). Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. Pacific Symposium on Biocomputing 7 (PSB 2002), 175-186. (Full text, Pubmed)